Adversarial generation of extreme samples

Modelling extreme events in order to evaluate and mitigate their risk is a fundamental goal in many areas, including extreme weather events, financial crashes, and unexpectedly high demand for online services. In order to mitigate such risk it is vital to be able to generate a wide range of extreme, and realistic, scenarios. Researchers from the National University of Singapore and IIT Bombay have developed an approach to do just that. In work recently posted on arXiv Siddharth Bhatia, Arjit Jain, and Bryan Hooi, note that in many applications, stress-testing is an important tool. This typically involves testing a system on a wide range of extreme but realistic scenarios to check that the system can cope in such situations.